The use of third parties, including vendors and suppliers, is integral in modern organizational strategies providing flexibility to scale, innovation, specialization, and operational efficiency. However, this increasing reliance on third parties brings with it potential risks that must be identified and remediated across the supplier’s relationship lifecycle. These risks span a number of domains including cyber, financial, reputational and operational to name a few.
At the same time, organizations are facing increasingly rigorous and constantly evolving regulations that require rapid reporting in case of a vendor breach or disruption. With a complex network of vendors and suppliers, a growing list of risks to be evaluated, and a changing regulatory and threat landscape to consider, how can procurement teams more proactively manage these risks, let alone keep up?
It’s impossible for procurement teams to keep pace with the volumes of data and changing regulations using traditional methods to assess suppliers such as spreadsheets and siloed tools. Many procurement teams must work with insufficient budgets or lack the staff expertise needed to handle vast amounts of risk data from a variety of sources. This puts organizations of all sizes at a disadvantage when it comes to planning for and remediating potential business risks due to supplier incidents. Artificial intelligence (AI) can help procurement leaders change that.
Reduce the time needed to find & remediate risks
AI helps procurement teams evaluate business risks far more quickly by identifying and prioritizing those risks, even across increasingly large and complex data sets. This is possible through a combination of automation, complex data analysis, and predictive analytics.
Using AI, you can automate the supplier risk assessment tasks you must complete on a regular basis, such as onboarding, compliance monitoring, completing audits, and reviewing unstructured evidence and contracts. Automation also helps procurement professionals analyze the large and growing volumes of risk information, provided they are using AI tools that have been trained using relevant historical data. This enables procurement teams to spend more time focusing on strategic supplier decisions and planning — the areas that require human attention and manual intervention.
Complex data analysis
If you have a large and growing quantity of suppliers, it stands to reason that you’ll need to analyze more data about those suppliers, preferably quickly to not slow the business down. Particularly when it comes to managing supplier risk effectively, AI can help by analyzing:
Cyber data to identify indicators of a possible data breach
Global sanctions and politically exposed persons lists to identify violations
Regulatory data to identify regulation changes or potential violations
Unstructured evidence including ISO certifications, SOC2 reports, contracts and policy documentation
Operational data to identify supply chain disruptions
Financial data to identify trends or predict cash flow problems
Additionally, machine learning (ML) is often embedded in reporting and analytics tools to make sense of large amounts of seemingly disconnected data in ways that would be extremely labor intensive, time consuming and error prone for humans to perform. Machine learning models aren’t set in stone; proper implementation enables organizations to continuously train the ML model using new data, resulting in more dynamic and realistic risk analysis that anticipates potential supplier threats.
How often do you hear people lamenting the amazing accuracy of hindsight? Predictive analytics helps you anticipate future potential risks by combining historical information with external variables, including international volatility, weather data and patterns, and market volatility to enable procurement teams to predict how those variables could impact supply chains. This approach empowers organizations to adopt a proactive risk management approach and minimize the potential impact of adverse events.
Improve threat analytics accuracy with AI
Everything that accelerates your ability to find and remediate risks can also help you improve the accuracy of your threat analytics. Because AI can automate the collection and processing of data and analyze that data for patterns, procurement teams can focus on more business-level tasks. Automating those tasks using AI also reduces potential human error, helping you ensure greater accuracy. And because AI can analyze large data sets quickly, you can review more data sources. This analysis makes it easier to identify patterns and discover anomalies that may indicate a threat, helping you to respond faster to a potential issue and thereby minimize overall business impact.
Reduce the workload of procurement teams
There’s no question that traditional methods of managing supplier risks are time consuming and error prone. Even the best procurement and risk managers may miss something when looking through multiple spreadsheets and entering data from a large number of sources. That inherently makes reports less accurate and certainly less timely. Using AI, your team can make risk management decisions regarding third-party vendors and suppliers based on accurate, comprehensive, and up to date information. It also enables a renewed focus on forecasting and risk evaluation based on how those risks could impact your organization meeting its overall objectives.
3 things to be aware of in AI solutions
AI has dominated the news over the past year, so much so that U.S. President Biden recently issued an executive order on safe, secure, and trustworthy AI. As the EO highlights, AI has considerable potential, but it is not without risk itself. Those organizations using AI tools must be sensitive to the potential risks of AI and choose solutions that address those risks. Here are a few that you should make sure your solution is controlling for:
AI hallucination: At times, AI could deliver an invalid interpretation as fact, due to statistical anomalies, bad input, or incompatible learning model data. Supplier risk management solutions that leverage AI must address this risk by ensuring that the model is trained on real supplier risk data that is accurate, diverse, and representative of real-world scenarios. In addition, these models must be continually tuned to ensure ongoing improvement based on context and nuances related primarily to supplier risk.
Cognitive bias: AI systems can be built using biased learning model data, inevitably resulting in biased responses. It can be hard to detect bias, which is why using diverse training data is so important. Your learning model data should reflect the real-world population. In addition, AI models must be updated on an ongoing basis to incorporate new data and reduce potential bias. Human reviewers can help identify bias in AI-generated content and responses, as well as assess the overall performance of the solution. Look for a solution provider that conducts audits of their AI models regularly.
Data security: Many organizations have already struggled with employees inputting proprietary data into large language models (LLMs). Unfortunately, that data can subsequently be shared outside the organization, putting intellectual property or sensitive business data at risk. LLM solutions may embed inputs into their data models that allow subsequent queries to that data, which is problematic for protecting confidential data. Look for an AI solution that encrypts sensitive data at rest and in transit as well as providing strong access controls and authorization mechanisms. This can help to ensure unauthorized individuals or systems are unable to access and manipulate the data.
While the third-party vendor and supplier landscape has become ever more complex in recent years due to regionalizing supply chains and outsourcing more operations, procurement and risk teams can leverage an appropriately trained and maintained AI solution to detect and reduce risk and improve risk management. By automating routine tasks, improving expertise, and delivering complex analysis, procurement and risk managers can manage supplier risks effectively using AI and concentrate instead on achieving strategic business activities.